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Abstract A Bayesian optimization procedure is presented for calibrating a multi-mechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8-9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600 °C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework [1]. Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. Validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.
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Improving the Interpretability of Physics-Based Bias in Material Models
Abstract In order to accurately predict the performance of materials under dynamic loading conditions, models have been developed that describe the rate-dependent material behavior and irrecoverable plastic deformation that occurs at elevated strains and applied loads. Most of these models have roots in empirical fits to data and, thus, require the addition of specific parameters that reflect the properties and response of specific materials. In this work, we present a systematic approach to the problem of calibrating a Johnson-Cook plasticity model for 304L stainless steel using experimental testing in which the parameters are treated as dependent on the state of the material and uncovered using experimental data. The results obtained indicate that the proposed approach can make the presence of a discrepancy term in calibration unnecessary and, at the same time, improve the prediction accuracy of the model into new input domains and provide improved understanding of model bias compared to calibration with stationary parameter values.
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- Award ID(s):
- 1633608
- PAR ID:
- 10341180
- Date Published:
- Journal Name:
- ASME 2020 Verification and Validation Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/VVS2020-8816
